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Publication
Classification of reticular pattern and streaks in dermoscopic images based on texture analysis
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Advisor(s)
Abstract(s)
The early detection of melanoma is one of the greatest challenges in clinical practice of dermatology,
and the reticular pattern is one of the most important dermoscopic structures to improve melanocytic lesion
diagnosis. A texture-based approach is developed for the automatic detection of reticular patterns, whose output
will assist clinical decision-making. Feature selection was based on the use of two algorithms by means of the
classical graylevel co-occurrence matrix and Laws energy masks optimized on a set of 104 dermoscopy images.
The AdaBoost (adaptive boosting) approach to machine learning was used within this strategy. Results suggest
superiority of LEM for reticular pattern detection in dermoscopic images, achieving a sensitivity of 90.16% and a
specificity of 86.67%. The use of automatic classification in dermoscopy to support clinicians is a strong tool to
assist diagnosis; however, the use of automatic classification as a complementary tool in clinical routine requires
algorithms with high levels of sensitivity and specificity. The results presented in this work will contribute to
achieving this goal.
Description
Article number 044503.
Keywords
pattern recognition melanoma reticular pattern dermoscopy
Citation
Machado, M., Pereira, J., & Fonseca-Pinto, R. (2015). Classification of reticular pattern and streaks in dermoscopic images based on texture analysis. Journal of Medical Imaging (Bellingham, Wash.), 2(4), 044503–044503. https://doi.org/10.1117/1.JMI.2.4.044503
Publisher
Society of Photo-optical Instrumentation Engineers